The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthe

Historic centers are often subject to urban renewal without the prior knowledge of the extent of the cohesion and attachment to place of its inhabitants. Identifying the rates of cohesion and place attachment can help urban designers to avoid decisions that lead to clashes with the reality of the social groups inhabiting the neighborhoods of the historic center. So the research aimed to measure cohesion and place attachment in a methodological approach based on a psychological instrument conducted by previous studies .The measurements were applied through a questionnaire given to the residents of six elected neighborhoods forming the historic center of Al- Adhamiya.The research assumed the relative disparity rates of cohesion and place atta

English teachers in Iraq and other countries around the world up to this time use traditional methods to help students memorize new vocabularies. The sense of words or structure is not to be given in either the native tongue or the target language by definition, but is induced by the way the form is used in the situation. Situational Language Teaching (SLT) has recently become the needed method for improving speaking skills. It indicates the application of learning the language in actual and social exchange expressions so as to let learners do as required in classroom and non-classroom circumstances. This pilot study seeks to answer the following questions: Can SLT improve English speaking skills of target learners? How SLT contribute to th

The article includes the concept of aesthetics through what has been dealt with in the literature by philosophers and researchers who have addressed this concept in an attempt to derive indicators of aesthetics. The article adopted a descriptive and analytical methodology by reviewing recent literature on the concept of aesthetics and its relation to urban planning and design issues. Many subjective and objective aesthetics indicators have been identified, some of which are classified under real aesthetics, and some under fake aesthetics. The indicators were applied to the Kufa Mosque complex and Sahla Mosque complex as a comparative case study. It was found that the indicators of real aesthetics have a higher weight in determining

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve